Research Objectives
This research was conducted during the ideation phase of MyStack.ai, an AI tool discovery platform designed to help users explore, compare, and organise AI tools based on their needs, working styles, and emotional state. The study aims to investigate:
User Behaviours and Routines
To understand how people currently use AI tools in their daily workflows, including:The number of tools they actively use
How and where they discover new tools
How users organise or manage AI tools over time
Emotional Experience of Tool Discovery
To examine whether users experience stress, overwhelm, or fatigue when navigating the expanding landscape of AI tools—especially in contexts where tools are poorly explained or fail to match the user's goals.Feature Relevance and Concept Validation
To assess the perceived value, clarity, and alignment of MyStack.ai’s three core features with real user pain points:Drop: A guided, mood-based exploration experience designed to reduce stress and support emotionally attuned discovery.
T-bot: A conversational AI trained on real user reviews and feedback to provide personalised tool suggestions, offering a more human-centred alternative to generic recommendation engines.
Stack: A pioneer concept enabling users to categorise, archive, and revisit their preferred AI tools in one place—something currently lacking in the fragmented AI ecosystem.
Pain Point Mapping and Design Opportunities
To identify common frustrations, gaps, and hidden patterns in user behaviour, in order to inform prioritisation of features and experience design. This includes examining how users currently track, compare, and revisit tools—and where MyStack.ai can meaningfully improve or replace these behaviours.
Ultimately, this research aims to ground MyStack.ai’s product development in authentic user insight—ensuring the platform delivers clarity, control, and confidence in the increasingly complex landscape of AI tool usage.
